International Journal of Computer Assisted Radiology and Surgery最新文献

{"title":"Training a deep learning model to predict the anatomy irradiated in fluoroscopic x-ray images.","authors":"Lunchi Guo, Dennis Trujillo, James R Duncan, M Allan Thomas","doi":"10.1007/s11548-025-03422-0","DOIUrl":"https://doi.org/10.1007/s11548-025-03422-0","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate patient dosimetry estimates from fluoroscopically-guided interventions (FGIs) are hindered by limited knowledge of the specific anatomy that was irradiated. Current methods use data reported by the equipment to estimate the patient anatomy exposed during each irradiation event. We propose a deep learning algorithm to automatically match 2D fluoroscopic images with corresponding anatomical regions in computational phantoms, enabling more precise patient dose estimates.</p><p><strong>Methods: </strong>Our method involves two main steps: (1) simulating 2D fluoroscopic images, and (2) developing a deep learning algorithm to predict anatomical coordinates from these images. For part (1), we utilized DeepDRR for fast and realistic simulation of 2D x-ray images from 3D computed tomography datasets. We generated a diverse set of simulated fluoroscopic images from various regions with different field sizes. In part (2), we employed a Residual Neural Network (ResNet) architecture combined with metadata processing to effectively integrate patient-specific information (age and gender) to learn the transformation between 2D images and specific anatomical coordinates in each representative phantom. For the Modified ResNet model, we defined an allowable error range of ± 10 mm.</p><p><strong>Results: </strong>The proposed method achieved over 90% of predictions within ± 10 mm, with strong alignment between predicted and true coordinates as confirmed by Bland-Altman analysis. Most errors were within ± 2%, with outliers beyond ± 5% primarily in Z-coordinates for infant phantoms due to their limited representation in the training data. These findings highlight the model's accuracy and its potential for precise spatial localization, while emphasizing the need for improved performance in specific anatomical regions.</p><p><strong>Conclusion: </strong>In this work, a comprehensive simulated 2D fluoroscopy image dataset was developed, addressing the scarcity of real clinical datasets and enabling effective training of deep-learning models. The modified ResNet successfully achieved precise prediction of anatomical coordinates from the simulated fluoroscopic images, enabling the goal of more accurate patient-specific dosimetry.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MIS-NeRF: neural radiance fields in minimally-invasive surgery.","authors":"Samad Barri Khojasteh, David Fuentes-Jimenez, Daniel Pizarro, Yamid Espinel, Adrien Bartoli","doi":"10.1007/s11548-025-03429-7","DOIUrl":"https://doi.org/10.1007/s11548-025-03429-7","url":null,"abstract":"<p><strong>Purpose: </strong>Minimally-invasive surgery (MIS) reduces the trauma compared to open surgery but is challenging for endophytic lesion localisation. Augmented reality (AR) is a promising assistance, which superimposes a preoperative 3D lesion model onto the MIS images. It requires solving the difficult problem of 3D model to MIS image registration. We propose MIS-NeRF, a neural radiance field (NeRF) which provides high-fidelity intraoperative 3D reconstruction, used to bootstrap iterative closest point (ICP) registration.</p><p><strong>Methods: </strong>Existing NeRF methods break down in MIS because of the moving light source and specular highlights. The proposed MIS-NeRF is adapted to these conditions. First, it incorporates the camera centre as an additional input to the radiance function, which allows MIS-NeRF to handle the moving light source. Second, it uses a modified volume rendering which handles specular highlights. Third, it uses a regularised compound loss to enhance surface reconstruction.</p><p><strong>Results: </strong>MIS-NeRF was tested on three synthetic datasets and retrospectively on four laparoscopic surgeries. It successfully reconstructed high-fidelity liver and uterus surfaces, reducing common artefacts including high-frequency noise and bumps caused by specular highlights. ICP registration achieved faithful alignment between the preoperative and intraoperative 3D models, with an average error of 3.25 mm, outperforming the second-best method by a <math><mrow><mn>15</mn> <mo>%</mo></mrow> </math> margin.</p><p><strong>Conclusion: </strong>MIS-NeRF improves AR-based lesion localisation by facilitating accurate 3D model registration to multiple MIS images.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model science and modelling informatics for a model identity card/certificate (MIC) in radiology and surgery.","authors":"Heinz U Lemke","doi":"10.1007/s11548-025-03431-z","DOIUrl":"https://doi.org/10.1007/s11548-025-03431-z","url":null,"abstract":"<p><strong>Purpose: </strong>A model identity card/certificate (MIC) aims at enhancing trustworthiness in systems that purport to provide intelligent responses to human questions and actions. It should serve those individuals who have a professional need for or who want to feel more comfortable, when writing about or interacting with intelligent machines. The general and/or specific domain models on which recommendations, decisions or actions of these systems are based, reflect in their MIC the level of model relevance, truthfulness and transparency.</p><p><strong>Methods: </strong>In the specific context of CARS, methods and tools for building models and their corresponding templates for a MIC in the domains of radiology and surgery should be drawn from relevant elements of a model science, specifically from mathematical modelling methods (e.g. for model truthfulness) and modelling informatics tools (e.g. for model transparency). Modelling methods for radiology and surgery may be drawn from applied mathematics, mathematical logic and/or syntax graph based text. Examples of supporting tools from modelling informatics are UML, MIMMS, model-based software engineering or model-based medical evidence.</p><p><strong>Results: </strong>For a Model Guided Precision Medicine as defined by SPIE MI 2025, a precise protocol relating to the origins of these models need to be reflected in the corresponding MIC templates for specific medical domains, for example, in radiology or surgery. An example of a MIC template (work-in-progress) in the domain of orthopaedic surgery serves to demonstrate some aspects of model relevance, truthfulness and transparency.</p><p><strong>Conclusion: </strong>Gaining trustworthiness in intelligent systems based on models and related AI tools is a challenging undertaking and raises many critical questions, specifically those related to ascertain model relevance, truthfulness and transparency. The healthcare system, in particular, will have to be concerned about the availability of digital identity certificates for these systems and related artefacts, e.g. digital twins, avatars, robots, intelligent agents, etc. Further development of the elements of a model science with emphasis on modelling informatics may be the right path to take, preferably in cooperation with international R&D groups interested in the realisation of an MGM and corresponding MICs.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of synthetic training data for 3D intraoral reconstruction of cleft patients from single images.","authors":"Lasse Lingens, Yoriko Lill, Prasad Nalabothu, Benito K Benitez, Andreas A Mueller, Markus Gross, Barbara Solenthaler","doi":"10.1007/s11548-025-03396-z","DOIUrl":"https://doi.org/10.1007/s11548-025-03396-z","url":null,"abstract":"<p><strong>Purpose: </strong>This study investigates the effectiveness of synthetic training data in predicting 2D landmarks for 3D intraoral reconstruction in cleft lip and palate patients. We take inspiration from existing landmark prediction and 3D reconstruction techniques for faces and demonstrate their potential in medical applications.</p><p><strong>Methods: </strong>We generated both real and synthetic datasets from intraoral scans and videos. A convolutional neural network was trained using a negative-Gaussian log-likelihood loss function to predict 2D landmarks and their corresponding confidence scores. The predicted landmarks were then used to fit a statistical shape model to generate 3D reconstructions from individual images. We analyzed the model's performance on real patient data and explored the dataset size required to overcome the domain gap between synthetic and real images.</p><p><strong>Results: </strong>Our approach generates satisfying results on synthetic data and shows promise when tested on real data. The method achieves rapid 3D reconstruction from single images and can therefore provide significant value in day-to-day medical work.</p><p><strong>Conclusion: </strong>Our results demonstrate that synthetic training data are viable for training models to predict 2D landmarks and reconstruct 3D meshes in patients with cleft lip and palate. This approach offers an accessible, low-cost alternative to traditional methods, using smartphone technology for noninvasive, rapid, and accurate 3D reconstructions in clinical settings.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144136383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving automatic cerebral 3D-2D CTA-DSA registration.","authors":"Charles Downs, P Matthijs van der Sluijs, Sandra A P Cornelissen, Frank Te Nijenhuis, Wim H van Zwam, Vivek Gopalakrishnan, Xucong Zhang, Ruisheng Su, Theo van Walsum","doi":"10.1007/s11548-025-03412-2","DOIUrl":"https://doi.org/10.1007/s11548-025-03412-2","url":null,"abstract":"<p><strong>Purpose: </strong>Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT), a revolutionary intervention for ischemic stroke, is limited by its reliance on 2D fluoroscopic imaging, which lacks depth and comprehensive vascular detail. We propose a novel AI-driven pipeline for 3D CTA to 2D DSA cross-modality registration, termed DeepIterReg.</p><p><strong>Methods: </strong>The proposed pipeline integrates neural network-based initialization with iterative optimization to align pre-intervention and peri-intervention data. Our approach addresses the challenges of cross-modality alignment, particularly in scenarios involving limited shared vascular structures, by leveraging synthetic data, vein-centric anchoring, and differentiable rendering techniques.</p><p><strong>Results: </strong>We assess the efficacy of DeepIterReg through quantitative analysis of capture ranges and registration accuracy. Results show that our method can accurately register 70% of a test set of 20 patients and can improve capture ranges when performing an initial pose estimation using a convolutional neural network.</p><p><strong>Conclusions: </strong>DeepIterReg demonstrates promising performance for 3D-to-2D stroke intervention image registration, potentially aiding clinicians by improving spatial understanding during EVT and reducing dependence on manual adjustments.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications.","authors":"Manuel Villa, Jaime Sancho, Gonzalo Rosa-Olmeda, Miguel Chavarrias, Eduardo Juarez, Cesar Sanz","doi":"10.1007/s11548-025-03416-y","DOIUrl":"https://doi.org/10.1007/s11548-025-03416-y","url":null,"abstract":"<p><strong>Purpose: </strong>This study proposes a generalization of markerless patient registration in image-guided neurosurgery based on depth information. The work builds on previous research to evaluate the performance of a range of commercial depth cameras and two different registration algorithms in this context.</p><p><strong>Methods: </strong>A multimodal experimental setup was used, testing five depth cameras in seven configurations. Fiducial registration error (FRE) and target registration error (TRE) metrics were calculated using iterative closest point (ICP) and deep global registration (DGR) algorithms. A phantom head model was used to simulate clinical conditions, with cameras positioned to capture the face and craniotomy regions.</p><p><strong>Results: </strong>The best-performing cameras, such as the D405 and Zed-M+, achieved TRE values as low as 2.36 ± 0.46 mm and 2.49 ± 0.35 mm, respectively, compared to manual registration that obtains a 1.37 mm error. Cameras equipped with texture projectors or enhanced depth refinement demonstrated improved performance. The proposed methodology effectively characterized the suitability of the camera for the registration tasks.</p><p><strong>Conclusion: </strong>This study validates an adaptable and reproducible framework to evaluate depth cameras in neurosurgical scenarios, highlighting D405 and Zed-M + as reliable options. Future work will focus on improving depth quality through hardware and algorithmic improvements. The experimental data and the accompanying code were made publicly available to ensure reproducibility.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep hashing for global registration of preoperative CT and video images for laparoscopic liver surgery.","authors":"Hanyuan Zhang, Sandun Bulathsinhala, Brian R Davidson, Matthew J Clarkson, João Ramalhinho","doi":"10.1007/s11548-025-03418-w","DOIUrl":"https://doi.org/10.1007/s11548-025-03418-w","url":null,"abstract":"<p><strong>Purpose: </strong>Registration of computed tomography (CT) to laparoscopic video images is vital to enable augmented reality (AR), a technology that holds the promise of minimising the risk of complications during laparoscopic liver surgery. Although several solutions have been presented in the literature, they always rely on an accurate initialisation of the registration that is either obtained manually or automatically estimated on very specific views of the liver. These limitations pose a challenge to the clinical translation of AR.</p><p><strong>Methods: </strong>We propose the use of a content-based image retrieval (CBIR) framework to obtain an automatic robust initialisation to the registration. Instead of directly registering video and CT, we render a dense set of possible views of the liver from CT and extract liver contour features. To reduce feature maps to lower dimension vectors, we use a deep hashing (DH) network that is trained in a triplet scheme. Registration is obtained by matching the intra-operative image hashing encoding to the closest encodings found in the pre-operative renderings.</p><p><strong>Results: </strong>We validate our method on synthetic and real data from a phantom and real patient data from eight surgeries. Phantom experiments show that registration errors acceptable for an initial registration are obtained if sufficient pre-operative solutions are considered. In seven out of eight patients, the method is able to obtain a clinically relevant alignment.</p><p><strong>Conclusion: </strong>We present the first work to adapt DH to the CT to video registration problem. Our results indicate that this framework can effectively replace manual initialisations in multiple views, potentially increasing the translation of these techniques.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A-MFST: adaptive multi-flow sparse tracker for real-time tissue tracking under occlusion.","authors":"Yuxin Chen, Zijian Wu, Adam Schmidt, Septimiu E Salcudean","doi":"10.1007/s11548-025-03414-0","DOIUrl":"https://doi.org/10.1007/s11548-025-03414-0","url":null,"abstract":"<p><strong>Purpose: </strong>Tissue tracking is critical for downstream tasks in robot-assisted surgery. The Sparse Efficient Neural Depth and Deformation (SENDD) model has previously demonstrated accurate and real-time sparse point tracking, but struggled with occlusion handling. This work extends SENDD to enhance occlusion detection and tracking consistency while maintaining real-time performance.</p><p><strong>Methods: </strong>We use the Segment Anything Model2 (SAM2) [1] to detect and mask occlusions by surgical tools, and we develop and integrate into SENDD an Adaptive Multi-Flow Sparse Tracker (A-MFST) with forward-backward consistency metrics, to enhance occlusion and uncertainty estimation. A-MFST is an unsupervised variant of the Multi-Flow dense Tracker (MFT) [2].</p><p><strong>Results: </strong>We evaluate our approach on the STIR dataset [3] and demonstrate a significant improvement in tracking accuracy under occlusion, reducing average tracking errors by 12% in Mean Endpoint Error (MEE) and showing a 6% improvement in <math><msubsup><mi>δ</mi> <mrow><mtext>avg</mtext></mrow> <mi>x</mi></msubsup> </math> , the averaged accuracy over thresholds of [4, 8, 16, 32, 64] pixels [4]. The incorporation of forward-backward consistency further improves the selection of optimal tracking paths, reducing drift and enhancing robustness. Notably, these improvements were achieved without compromising the model's real-time capabilities.</p><p><strong>Conclusions: </strong>Using A-MFST and SAM2, we enhance SENDD's ability to track tissue in real-time, under instrument and tissue occlusions. Our approach improves tracking accuracy and reliability by integrating SAM2 for robust occlusion handling and employing forward-backward consistency for optimal frame selection. Experimental results on the STIR dataset demonstrate that A-MFST reduces tracking errors while preserving real-time performance, making it well suited for surgical applications. Future work will focus on further refining adaptive mechanisms to enhance robustness and computational efficiency.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the generalizability of video-based assessment of intraoperative surgical skill in capsulorhexis.","authors":"Zhiwei Gong, Bohua Wan, Jay N Paranjape, Shameema Sikder, Vishal M Patel, S Swaroop Vedula","doi":"10.1007/s11548-025-03406-0","DOIUrl":"https://doi.org/10.1007/s11548-025-03406-0","url":null,"abstract":"<p><strong>Purpose: </strong>Assessment of intraoperative surgical skill is necessary to train surgeons and certify them for practice. The generalizability of deep learning models for video-based assessment (VBA) of surgical skill has not yet been evaluated. In this work, we evaluated one unsupervised domain adaptation (UDA) and three semi-supervised (SSDA) methods for generalizability of models for VBA of surgical skill in capsulorhexis by training on one dataset and testing on another.</p><p><strong>Methods: </strong>We used two datasets, D99 and Cataract-101 (publicly available), and two state-of-the-art models for capsulorhexis. The models include a convolutional neural network (CNN) to extract features from video images, followed by a long short-term memory (LSTM) network or a transformer. We augmented the CNN and the LSTM with attention modules. We estimated accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).</p><p><strong>Results: </strong>Maximum mean discrepancy (MMD) did not improve generalizability of CNN-LSTM but slightly improved CNN transformer. Among the SSDA methods, Group Distributionally Robust Supervised Learning improved generalizability in most cases.</p><p><strong>Conclusion: </strong>Model performance improved with the domain adaptation methods we evaluated, but it fell short of within-dataset performance. Our results provide benchmarks on a public dataset for others to compare their methods.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring interaction paradigms for segmenting medical images in virtual reality.","authors":"Zachary Jones, Simon Drouin, Marta Kersten-Oertel","doi":"10.1007/s11548-025-03424-y","DOIUrl":"https://doi.org/10.1007/s11548-025-03424-y","url":null,"abstract":"<p><strong>Purpose: </strong>Virtual reality (VR) can offer immersive platforms for segmenting complex medical images to facilitate a better understanding of anatomical structures for training, diagnosis, surgical planning, and treatment evaluation. These applications rely on user interaction within the VR environment to manipulate and interpret medical data. However, the optimal interaction schemes and input devices for segmentation tasks in VR remain unclear. This study compares user performance and experience using two different input schemes.</p><p><strong>Methods: </strong>Twelve participants segmented 6 CT/MRI images using two input methods: keyboard and mouse (KBM) and motion controllers (MCs). Performance was assessed using accuracy, completion time, and efficiency. A post-task questionnaire measured users' perceived performance and experience.</p><p><strong>Results: </strong>No significant overall time difference was observed between the two input methods, though KBM was faster for larger segmentation tasks. Accuracy was consistent across input schemes. Participants rated both methods as equally challenging, with similar efficiency levels, but found MCs more enjoyable to use.</p><p><strong>Conclusion: </strong>These findings suggest that VR segmentation software should support flexible input options tailored to task complexity. Future work should explore enhancements to motion controller interfaces to improve usability and user experience.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}